Proper intervention after the onset of cardiac ischemia is critical to minimize permanent injury to the myocardium. The basic decision the clinician faces is whether or not to perform aggressive revascularization such as by-pass surgery or angioplasty. One piece of information a clinician needs is a measure of local myocardial wall function. Currently, the local myocardial wall function is determined by an echo stress test. While this procedure is sensitive to ischemic disease, it is highly subjective and not quantitative. MR tagging techniques have shown great potential for measuring quantifiable 3-D strain in an in vivo left ventricle, but have remained in the research setting primarily because of the time and manual intervention required to identify the left-ventricle (LV) contours in each of the 200 images in a typical study. For MR tagging techniques to be clinically viable, a strain map must be reconstructed quickly with minimal user interaction. The overall goal of the proposed research project is to develop fast unsupervised algorithms for reconstructing a strain map from MR image data. We hypothesize that (1) a 3-D strain map can be reconstructed within 3 minutes after the scan, and (2) a sequence of single slice radial and circumferential strain maps can be reconstructed quickly enough for an MRI cardiac stress test (approximately 1-2 seconds per time frame after image acquisition). To test this hypothesis, we propose to: 1) Develop unsupervised algorithms for tracking tag lines without pre-defined myocardial contours. 2) Develop unsupervised algorithms for jointly reconstructing strain and identifying myocardial contours. 3) Develop parallel processing versions of the algorithms in Specific Aims 1 and 2.